Rippit vs. Gong for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both

Short answer: Gong is the system of record for sales calls and revenue workflows: forecasting, deal management and rep coaching.[1]

Rippit is built for one job: analyzing and acting on every customer conversation at scale.[2] It reads every conversation customers have with your company, across sales, support, success and chatbots, turns what it finds into structured data,[3] and runs AI agents that monitor, analyze and act on that data.[2] Any team can define a new field in plain English, combine it with others and keep drilling until it reaches the root cause.[4]

Many teams use both: they keep Gong for revenue workflows and add Rippit to ask questions across the whole customer journey.[5] Rippit has a free plan.[6] Gong pricing is by quote.[7]

Where Gong and Rippit overlap is in analysis and Agents you build on top of sales calls — Rippit is better suited to sit across multiple data sources when you have conversation data that also lives outside of Gong.

How does Rippit analyze every conversation?

1,000 conversations or 1,000,000, sales calls and support tickets alike: Rippit reads every one.[2][10]

Rippit runs an AI engine built to label conversations in bulk: AI enriches every conversation[2] and stores the results as structured, queryable data,[3] so analysis and agents can work across all of it. Reading every call and ticket doesn't have to be expensive. In Rippit's own coverage benchmark, it read all 1,000 transcripts in full for about six cents, the same full read that cost about $61 per question with a map-reduce approach.[23] Questions then run on those stored fields across the full dataset, instead of rereading transcripts each time, so asking the same question again returns the same answer.[23] For open-ended questions no existing field answers, Rippit also runs ad hoc deep dives[3] on up to 10,000 conversations at a time, and what they find can feed new analysis and agents.

That changes three things:

Can you use Rippit and Gong together?

Yes. Rippit connects to Gong and Granola as well as Zendesk and Intercom, so sales calls sit next to support tickets and chats in one analysis.[10] You keep Gong for forecasting, deal reviews and coaching, and use Rippit to ask questions that span the whole customer journey. Rippit does the mass-scale enrichment, analyzing every conversation in scope[2] and storing each judgment as a field on that conversation,[4] and runs ad hoc deep dives on up to 10,000 conversations at a time for questions no existing field answers yet.[3]

In Rippit's review of 2,345 of its own sales conversations from 2025–2026, 133 prospects said their company already uses Gong, most often for sales calls.[5] A recurring ask was to analyze those calls alongside support tickets and chats, so product feedback and churn signals show up across the whole customer journey.[5]

Using both fixes two common gaps:

• Sales and support conversations live in different tools. Gong holds what was said before the sale, and your helpdesk holds what happened after. Rippit brings the ticket and chat history into the same analysis as the calls.[10]
• Churn signals show up after the handoff. Checkr combined Rippit with Snowflake operational data to find churn signals.[17] Brex feeds predicted CSAT and churn signals from Rippit into its customer success workflows.[18]

How is Rippit's custom analysis different from Gong AI Trackers?

Gong AI Trackers find a concept by meaning rather than exact words. A tracker for "asking for a discount" catches "Is that the best you can do?"[13] Admins build trackers with a question-based builder, and they run across calls and emails.[12] When you apply a tracker to past calls, Gong backfills a 14- or 30-day window.[12]

Rippit starts from a plain description of the insight you want and adds it as a field across every conversation.[3] One pass tags each conversation across 10+ dimensions, including intent, root cause, sentiment, urgency, churn signal and resolution, and a new category applies to past conversations in a single run.[4] For cross-journey questions, you define a field like "promise made in sales" or "onboarding friction" once and read it across sales calls, tickets and chats.

This speed pays off with unplanned questions. When Checkr's CEO asked a late-night question about background check issues in a specific state, the team delivered a detailed report by the next morning.[17]

Do Gong and Rippit have an MCP server?

Yes. Rippit supports MCP, so AI assistants like Claude can query your conversation data directly.[15] You ask in Claude, and Rippit runs the analysis across your full conversation dataset.[19]

This matters at scale. A general-purpose assistant can only hold a limited amount of text in its context window at one time,[20] so on its own it has to sample, split or summarize large sets of conversations. Rippit runs the analysis across every conversation and gives Claude the results, so every number traces back to specific conversations.

Gong also offers an official MCP server.[14] Its read-only tools answer questions about a single account or deal and generate account or deal briefs, and a Gong Technical Administrator creates the integration.[14] Use Gong's server for deal and account questions, and Rippit's for analysis across every conversation.

How much do Rippit and Gong cost?

Rippit publishes its pricing, and paid plans bill AI credits at cost.[6] Gong doesn't publish its prices; buyers request a custom proposal.[7]

What do teams use Rippit for?

• Brex replaced manual QA sampling with AI review of 100% of conversations and found churn risk in 3% of them. It built custom AI classifiers for onboarding friction, product gaps, sentiment and competitors, and feeds predicted CSAT and churn signals into CSM workflows.[18]
• Checkr broke dissatisfaction down into specific "atomic problems" and combined Rippit with Snowflake operational data to find churn signals. Checkr says its time from insight to action went "from weeks to hours."[17]
• Klaviyo went from reviewing fewer than 2% of its 600,000+ annual support incidents to analyzing 2.5 million conversations that had never been looked at. It analyzed a full month of product-launch conversations in two hours and uses Rippit for churn pattern analysis and chatbot performance monitoring.[21]

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